Bibliographic record
Abstract
Purpose To examine Mary Parker Follett's writings with respect to organizational justice and highlight insights that can advance contemporary organizational justice theory as well as help justice scholars effectively address challenges currently facing the field. Design/methodology/approach By comparing and contrasting Follett's writings with contemporary research, the author argues that Follett provides a number of insights that can advance contemporary justice theory and research. Discusses ways in which the field can capitalize on these insights. Findings Follett foreshadowed a number of important justice issues that have subsequently captured the attention of contemporary justice scholars. More importantly, her process‐oriented perspective suggests a number of research avenues that have yet to be fully explored including emotionality of injustice, integrative unity, and circular responses. In order to take advantage of Follett's insights, however, contemporary justice researchers may need to re‐examine current assumptions about: the nature of organizational justice; the way that it should be studied; and the relationship between theory and practice. Originality/value This paper is the first to examine Follett's writings in the context of organizational justice. Although the field of organizational justice has not yet recognized Follett's work, her writings deal both explicitly and implicitly with the concept of justice in considerable depth. Not only does Follett foreshadow contemporary research, but her writings also provide alternative avenues for theory development and research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".